Involves determining the optimal alternative of DASs, among multiple, conflicting, and interactive criteria of each DAS by using MCDM methods (FDOSM)

Data Acquisition Systems, often abbreviated to DAS, are systems designed to measure and track some form of physical system and convert this data into a form that can be viewed and manipulated on a computer. 

In the academic literature many studies use datasets without standardization, some of these studies collect data from professional Data Acquisition System (DAS), but other studies collect data by mobile application or from simple sensors like GPS device, personal camera …etc. 
So, for this reason a motivation of setting a standardization framework was rise to unified the method of collecting best dataset. 

A novel CP transfusion intelligent framework for rescuing COVID-19, patients across centralised/decentralised telemedicine hospitals based on the matching component process to provide an efficient CP from eligible donors to the most critical patients.

Development of CPs Transfusion Intelligent Framework:

• Adopt Decision Matrix Based on “COVID-19 Serological/Protein Biomarkers Criteria” and List of Patients
• Adopt Decision Matrix Based on “COVID19 Serological/Protein Biomarkers Criteria” and List of Donors
• AHP To Set Weights For COVID-19 serological/protein biomarkers Criteria by three experts
• TOPSIS To Prioritize Patients/Donors Based on AHP Weights
• Matching Intelligent Components

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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that impairs an individual’s social, communication and learning abilities. Every year, the World Health Organization diagnoses autism globally in one out of every 160 children.

The diagnosis of ASD is very important in the early stages of life, which prompted researchers to intensify efforts to reach the best solutions.

The research methodology can discuss the direction of the thesis in three phases. Firstly, the identification and preprocessing of the ASD dataset to prepare the data for the next phase. After that, the second phase is the development of hybrid diagnosis models constructed based on feature selection approaches and ML algorithms. Finally, the third phase is the evaluation and benchmarking framework for the developed hybrid diagnosis ML models.

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